LLMs, Truth, and Democracy: An Overview of Risks

Area of Science:

• Philosophy of AI
• Political Philosophy
• Epistemology

Background:

• Growing public concern exists regarding the impact of Artificial Intelligence (AI), particularly Large Language Models (LLMs), on truth and knowledge.
• Despite these concerns, systematic philosophical analysis of the political implications of AI's impact on truth is limited.

Purpose of the Study:

• To provide a philosophical analysis of truth-related risks associated with LLMs.
• To examine the political implications of these risks for democratic societies.
• To explore potential solutions, focusing on education.

Main Methods:

• Overview of truth-related risks posed by LLMs: hallucination, misinformation, epistemic bubbles, bullshit, relativism, epistemic anachronism, and epistemic incest.
• Philosophical argumentation to connect epistemic risks to democratic deficits.
• Reflection on potential interventions, particularly educational strategies.

Main Results:

• LLMs introduce significant epistemic risks, including hallucination and the creation of epistemic bubbles.
• These epistemic challenges undermine the foundational knowledge base necessary for robust democratic functioning.
• Non-minimalist theories of democracy are particularly vulnerable to these AI-driven epistemic disruptions.

Conclusions:

• The proliferation of LLMs presents substantial political-epistemic risks that threaten democratic foundations.
• Addressing these risks requires a multi-faceted approach, with education identified as a crucial site for intervention.
• Further philosophical and political analysis is needed to navigate the complex relationship between AI, truth, and democracy.